library(tidyverse)
library(ggformula)
library(janitor)
library(broom)
library(readxl)
library(jsonlite)
library(gprofiler2)
library(mclust)
theme_set(theme_bw())
set.seed(666)gvc_agora_opentargets
Setup environment
Read and prep data
GVC genes (within 1Mb flanking regions of GVC loci) [OLD]
gvc <-
read_xlsx("GVC_1Mb_comparison_050224.xlsx") %>%
clean_names() %>%
separate(gene_id, c("ensembl_gene_id", "version")) %>%
select(-version, -agora_nominated_list, -opentarget_info)
gvcgvc.genes <-
gvc %>%
arrange(absolute_distance) %>%
distinct(ensembl_gene_id, .keep_all = TRUE) %>%
select(ensembl_gene_id, gene_symbol, absolute_distance) %>%
arrange(gene_symbol)
gvc.genesgvc.genes %>% distinct(gene_symbol) %>% nrow()[1] 1344
GVC genes (within 1Mb flanking regions of GVC loci) minus APOE and HLA loci genes
Remove genes in APOE and HLA loci and manually add APOE and HLA genes (based on Bellenguez2022):
gvc.genes.apoe_hla <- gvc.genes %>% filter(ensembl_gene_id %in% c("ENSG00000130203", "ENSG00000196735", "ENSG00000179344", "ENSG00000196126"))
gvc.genes.apoe_hlagvc.minus_apoe_hla <- gvc %>% filter(grouped_loci_gvc != "APOE / TOMM40" & grouped_loci_gvc != "HLA")
gvc.minus_apoe_hlagvc.genes.minus_apoe_hla <-
gvc.minus_apoe_hla %>%
arrange(absolute_distance) %>%
distinct(ensembl_gene_id, .keep_all = TRUE) %>%
select(ensembl_gene_id, gene_symbol, absolute_distance) %>%
bind_rows(gvc.genes.apoe_hla) %>%
arrange(gene_symbol)
gvc.genes.minus_apoe_hlaAgora genes
Alzheimer’s disease gene prioritization scores from Agora (see also related journal article):
ago1 <- read_json("agora.syn25741025.overall_scores.v12.2024-10-24.json", simplifyVector = TRUE) %>% as_tibble()
ago1Alzheimer’s disease genes (Agora nominated targets):
https://agora.adknowledgeportal.org/genes/nominated-targets
ago2 <- read_csv("agora.nominated-targets.gene-list.2024-10-24.csv")
ago2ago <- ago1 # %>% filter(hgnc_symbol %in% ago2$`Gene Symbol`)
agoGenetics score
ago %>%
drop_na(genetics_score) %>%
gf_density(~ genetics_score, fill = "red", bins = 20) %>% gf_labs(title = "Agora genetics_score")
gmm_fit <- Mclust(ago %>% drop_na(genetics_score) %>% pull(genetics_score), G = 1:6) # 1 to 6 components
gmm_fit$parameters$mean 1 2 3 4 5 6
0.1777 0.7411 1.1681 1.5375 1.9040 2.3111
sqrt(gmm_fit$parameters$variance$sigmasq)[1] 0.1687
Multi-omics score
ago %>% drop_na(multi_omics_score) %>% gf_density(~ multi_omics_score, fill = "green", bins = 20) %>% gf_labs(title = "Agora multi_omics_score")
gmm_fit <- Mclust(ago %>% drop_na(multi_omics_score) %>% pull(multi_omics_score), G = 1:6) # 1 to 6 components
gmm_fit$parameters$mean 1 2 3 4 5 6
0.004475 0.432376 0.781154 1.128750 1.457576 1.899564
sqrt(gmm_fit$parameters$variance$sigmasq)[1] 0.07474
Target risk score
ago %>% drop_na(target_risk_score) %>% gf_density(~ target_risk_score, fill = "blue", bins = 20) %>% gf_labs(title = "Agora target_risk_score")
gmm_fit <- Mclust(ago %>% drop_na(target_risk_score) %>% pull(target_risk_score), G = 1:6) # 1 to 6 components
gmm_fit$parameters$mean 1 2 3 4 5 6
0.9669 0.7200 1.3287 2.0231 2.8133 3.4076
sqrt(gmm_fit$parameters$variance$sigmasq)[1] 0.5048 0.1405 0.3385 0.3090 0.4014 0.4202
OpenTargets genes
Alzheimer’s disease
Alzheimer’s disease gene prioritization scores from OpenTargets:
ot <- read_tsv("OT-MONDO_0004975-associated-targets-6_4_2024-v24_03.tsv", show_col_types = FALSE, na = "No data")
# ot <- read_tsv("OT-MONDO_0004975-associated-targets-10_24_2024-v24_09.tsv", show_col_types = FALSE, na = "No data")
otAdd Ensembl Gene IDs (WTF!):
otcols <- colnames(ot)
otensg <- gconvert(
query = ot$symbol,
organism = "hsapiens",
target= "ENSG",
mthreshold = Inf,
filter_na = TRUE) %>%
mutate(input_number = as.character(input_number)) %>%
left_join(ot %>% rownames_to_column(var = "input_number"), by = "input_number") %>%
select(ensembl_gene_id = target, otcols)
otensgParkinson’s disease
Parkinson’s disease gene prioritization scores from OpenTargets:
ot.pd <- read_tsv("OT-MONDO_0005180-associated-targets-1_9_2025-v24_09.tsv", show_col_types = FALSE, na = "No data")
ot.pdAdd Ensembl Gene IDs (WTF!):
otcols.pd <- colnames(ot.pd)
otensg.pd <- gconvert(
query = ot.pd$symbol,
organism = "hsapiens",
target= "ENSG",
mthreshold = Inf,
filter_na = TRUE) %>%
mutate(input_number = as.character(input_number)) %>%
left_join(ot.pd %>% rownames_to_column(var = "input_number"), by = "input_number") %>%
select(ensembl_gene_id = target, otcols.pd)
otensg.pdMyocardial infarction
Myocardial infarction gene prioritization scores from OpenTargets:
ot.mi <- read_tsv("OT-EFO_0000612-associated-targets-1_9_2025-v24_09.tsv", show_col_types = FALSE, na = "No data")
ot.miAdd Ensembl Gene IDs (WTF!):
otcols.mi <- colnames(ot.mi)
otensg.mi <- gconvert(
query = ot.mi$symbol,
organism = "hsapiens",
target= "ENSG",
mthreshold = Inf,
filter_na = TRUE) %>%
mutate(input_number = as.character(input_number)) %>%
left_join(ot.mi %>% rownames_to_column(var = "input_number"), by = "input_number") %>%
select(ensembl_gene_id = target, otcols.mi)
otensg.miAll protein coding genes in the human genome
gencode <-
data.frame(rtracklayer::import("gencode.v47.basic.annotation.gtf.gz")) %>%
distinct(gene_id, .keep_all = TRUE) %>%
filter(gene_type == "protein_coding") %>%
mutate(ensembl_gene_id = str_remove(gene_id, pattern = "\\.\\d+$")) %>%
select(ensembl_gene_id, gene_name) %>%
slice_sample(n = nrow(.), replace = FALSE) # permute rows
gencode %>% write_tsv("alt/gorilla/gencode.v47.basic.annotation.genes.protein_coding.tsv")ORA of GVC genes
GVC genes (within 1Mb flanking regions of GVC loci)
Important
unordered query
d0 <- gvc.genes %>% select(ensembl_gene_id, gene_symbol)
d0query <- d0 %>% distinct(ensembl_gene_id) %>% pull(ensembl_gene_id)
gostres <- gost(
query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = FALSE, # <- UNORDERED QUERY!
significant = TRUE,
user_threshold = 0.005,
sources = c("GO:BP", "KEGG", "REAC", "WP", "HP"),
correction_method = "gSCS")
gostres$result %>%
select(term_name, term_id, source, everything()) %>%
filter(term_size >= 5, term_size <= 350, intersection_size >= 3)gostplot(gostres, capped = FALSE, interactive = TRUE)GVC genes (within 1Mb flanking regions of GVC loci) minus APOE and HLA loci genes
Important
unordered query
d0.minus_apoe_hla <- gvc.genes.minus_apoe_hla %>% select(ensembl_gene_id, gene_symbol)
d0.minus_apoe_hlaquery <- d0.minus_apoe_hla %>% distinct(ensembl_gene_id) %>% pull(ensembl_gene_id)
gostres <- gost(
query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = FALSE, # <- UNORDERED QUERY!
significant = TRUE,
user_threshold = 0.005,
sources = c("GO:BP", "KEGG", "REAC", "WP", "HP"),
correction_method = "gSCS")
gostres$result %>%
select(term_name, term_id, source, everything()) %>%
filter(term_size >= 5, term_size <= 350, intersection_size >= 3)gostplot(gostres, capped = FALSE, interactive = TRUE)GVC genes (within 200Kb flanking regions of GVC loci) minus APOE and HLA loci genes
Important
unordered query
d0.minus_apoe_hla.200kb <- gvc.genes.minus_apoe_hla %>% filter(absolute_distance <= 200000) %>% select(ensembl_gene_id, gene_symbol)
d0.minus_apoe_hla.200kbquery <- d0.minus_apoe_hla.200kb %>% distinct(ensembl_gene_id) %>% pull(ensembl_gene_id)
gostres <- gost(
query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = FALSE, # <- UNORDERED QUERY!
significant = TRUE,
user_threshold = 0.005,
sources = c("GO:BP", "KEGG", "REAC", "WP", "HP"),
correction_method = "gSCS")
gostres$result %>%
select(term_name, term_id, source, everything()) %>%
filter(term_size >= 5, term_size <= 350, intersection_size >= 3)gostplot(gostres, capped = FALSE, interactive = TRUE)GVC genes (within 20Kb flanking regions of GVC loci) minus APOE and HLA loci genes
Important
unordered query
d0.minus_apoe_hla.20kb <- gvc.genes.minus_apoe_hla %>% filter(absolute_distance <= 20000) %>% select(ensembl_gene_id, gene_symbol)
d0.minus_apoe_hla.20kbquery <- d0.minus_apoe_hla.20kb %>% distinct(ensembl_gene_id) %>% pull(ensembl_gene_id)
gostres <- gost(
query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = FALSE, # <- UNORDERED QUERY!
significant = TRUE,
user_threshold = 0.005,
sources = c("GO:BP", "KEGG", "REAC", "WP", "HP"),
correction_method = "gSCS")
gostres$result %>%
select(term_name, term_id, source, everything()) %>%
filter(term_size >= 5, term_size <= 350, intersection_size >= 3)gostplot(gostres, capped = FALSE, interactive = TRUE)GVC genes (within 1Mb flanking regions of GVC loci) minus APOE and HLA loci genes, ordered by absolute distance from GVC loci
Important
query ordered by absolute distance
d0.minus_apoe_hla <- gvc.genes.minus_apoe_hla %>% arrange(absolute_distance) %>% select(ensembl_gene_id, gene_symbol, absolute_distance)
d0.minus_apoe_hlaquery <- d0.minus_apoe_hla %>% distinct(ensembl_gene_id, .keep_all = TRUE) %>% pull(ensembl_gene_id)
writeLines(query, "alt/enrichmentmap/gvc.genes.minus_apoe_hla.absolute_distance.query.txt")
multiquery <- c("> gvc.genes.minus_apoe_hla.absolute_distance", query)
gostres <- gost(
query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE, # <- ORDERED QUERY!
significant = TRUE,
user_threshold = 0.005,
sources = c("GO:BP", "KEGG", "REAC", "WP", "HP"),
correction_method = "gSCS")
gostres$result %>%
select(term_name, term_id, source, everything()) %>%
filter(term_size >= 5, term_size <= 350, intersection_size >= 3)gostplot(gostres, capped = FALSE, interactive = TRUE)ORA of Agora genes
Agora genes sorted by genetics_score
d5 <- ago %>%
drop_na(genetics_score) %>%
filter(genetics_score >= quantile(genetics_score, 0.90)) %>%
sample_frac(1L) %>% # randomize row order before arranging
arrange(desc(genetics_score))
d5query <- d5 %>% distinct(ensembl_gene_id) %>% pull(ensembl_gene_id)
writeLines(query, "alt/enrichmentmap/agora.genes.genetics_score.query.txt")
multiquery <- c(multiquery, "> agora.genes.genetics_score", query)
gostres <- gost(
query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
sources = c("GO:BP", "KEGG", "REAC", "WP", "HP"),
correction_method = "gSCS")
gostres$result %>%
select(term_name, term_id, source, everything()) %>%
filter(term_size >= 5, term_size <= 350, intersection_size >= 3)gostplot(gostres, capped = FALSE, interactive = TRUE)Agora genes sorted by multi_omics_score
d6 <- ago %>%
drop_na(multi_omics_score) %>%
filter(multi_omics_score >= quantile(multi_omics_score, 0.90)) %>%
sample_frac(1L) %>% # randomize row order before arranging
arrange(desc(multi_omics_score))
d6query <- d6 %>% distinct(ensembl_gene_id) %>% pull(ensembl_gene_id)
writeLines(query, "alt/enrichmentmap/agora.genes.multi_omics_score.query.txt")
multiquery <- c(multiquery, "> agora.genes.multi_omics_score", query)
gostres <- gost(
query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
sources = c("GO:BP", "KEGG", "REAC", "WP", "HP"),
correction_method = "gSCS")
gostres$result %>%
select(term_name, term_id, source, everything()) %>%
filter(term_size >= 5, term_size <= 350, intersection_size >= 3)gostplot(gostres, capped = FALSE, interactive = TRUE)Agora genes sorted by target_risk_score
d7 <- ago %>%
drop_na(target_risk_score) %>%
filter(target_risk_score >= quantile(target_risk_score, 0.90)) %>%
sample_frac(1L) %>% # randomize row order before arranging
arrange(desc(target_risk_score))
d7query <- d7 %>% distinct(ensembl_gene_id, .keep_all = TRUE) %>% pull(ensembl_gene_id)
writeLines(query, "alt/enrichmentmap/agora.genes.target_risk_score.query.txt")
multiquery <- c(multiquery, "> agora.genes.target_risk_score", query)
gostres <- gost(
query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
sources = c("GO:BP", "KEGG", "REAC", "WP", "HP"),
correction_method = "gSCS")
gostres$result %>%
select(term_name, term_id, source, everything()) %>%
filter(term_size >= 5, term_size <= 350, intersection_size >= 3)gostplot(gostres, capped = FALSE, interactive = TRUE)ORA of OpenTargets genes
Alzheimer’s disease
OpenTargets genes sorted by otGeneticsPortal
d8 <- otensg %>%
drop_na(otGeneticsPortal) %>%
sample_frac(1L) %>% # randomize row order before arranging
arrange(desc(otGeneticsPortal))
d8query <- d8 %>% distinct(ensembl_gene_id, .keep_all = TRUE) %>% pull(ensembl_gene_id)
writeLines(query, "alt/enrichmentmap/opentargets.genes.genetics_score.query.txt")
multiquery <- c(multiquery, "> opentargets.genes.genetics_score", query)
gostres <- gost(
query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
sources = c("GO:BP", "KEGG", "REAC", "WP", "HP"),
correction_method = "gSCS")
gostres$result %>%
select(term_name, term_id, source, everything()) %>%
filter(term_size >= 5, term_size <= 350, intersection_size >= 3)gostplot(gostres, capped = FALSE, interactive = TRUE)OpenTargets genes sorted by globalScore
d9 <- otensg %>%
drop_na(globalScore) %>%
filter(globalScore >= quantile(globalScore, 0.90)) %>%
sample_frac(1L) %>% # randomize row order before arranging
arrange(desc(globalScore))
d9query <- d9 %>% distinct(ensembl_gene_id, .keep_all = TRUE) %>% pull(ensembl_gene_id)
writeLines(query, "alt/enrichmentmap/opentargets.genes.global_score.query.txt")
multiquery <- c(multiquery, "> opentargets.genes.global_score", query)
gostres <- gost(
query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
sources = c("GO:BP", "KEGG", "REAC", "WP", "HP"),
correction_method = "gSCS")
gostres$result %>%
select(term_name, term_id, source, everything()) %>%
filter(term_size >= 5, term_size <= 350, intersection_size >= 3)gostplot(gostres, capped = FALSE, interactive = TRUE)Parkinson’s disease
OpenTargets genes sorted by otGeneticsPortal
d8.pd <- otensg.pd %>%
drop_na(otGeneticsPortal) %>%
sample_frac(1L) %>% # randomize row order before arranging
arrange(desc(otGeneticsPortal))
d8.pdquery <- d8.pd %>% distinct(ensembl_gene_id, .keep_all = TRUE) %>% pull(ensembl_gene_id)
writeLines(query, "alt/enrichmentmap/opentargets.pd.genes.genetics_score.query.txt")
multiquery <- c(multiquery, "> opentargets.pd.genes.genetics_score", query)
gostres <- gost(
query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
sources = c("GO:BP", "KEGG", "REAC", "WP", "HP"),
correction_method = "gSCS")
gostres$result %>%
select(term_name, term_id, source, everything()) %>%
filter(term_size >= 5, term_size <= 350, intersection_size >= 3)gostplot(gostres, capped = FALSE, interactive = TRUE)OpenTargets genes sorted by globalScore
d9.pd <- otensg.pd %>%
drop_na(globalScore) %>%
filter(globalScore >= quantile(globalScore, 0.90)) %>%
sample_frac(1L) %>% # randomize row order before arranging
arrange(desc(globalScore))
d9.pdquery <- d9.pd %>% distinct(ensembl_gene_id, .keep_all = TRUE) %>% pull(ensembl_gene_id)
writeLines(query, "alt/enrichmentmap/opentargets.pd.genes.global_score.query.txt")
multiquery <- c(multiquery, "> opentargets.pd.genes.global_score", query)
gostres <- gost(
query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
sources = c("GO:BP", "KEGG", "REAC", "WP", "HP"),
correction_method = "gSCS")
gostres$result %>%
select(term_name, term_id, source, everything()) %>%
filter(term_size >= 5, term_size <= 350, intersection_size >= 3)gostplot(gostres, capped = FALSE, interactive = TRUE)Myocardial infarction
OpenTargets genes sorted by otGeneticsPortal
d8.mi <- otensg.mi %>%
drop_na(otGeneticsPortal) %>%
sample_frac(1L) %>% # randomize row order before arranging
arrange(desc(otGeneticsPortal))
d8.miquery <- d8.mi %>% distinct(ensembl_gene_id, .keep_all = TRUE) %>% pull(ensembl_gene_id)
writeLines(query, "alt/enrichmentmap/opentargets.mi.genes.genetics_score.query.txt")
multiquery <- c(multiquery, "> opentargets.mi.genes.genetics_score", query)
gostres <- gost(
query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
sources = c("GO:BP", "KEGG", "REAC", "WP", "HP"),
correction_method = "gSCS")
gostres$result %>%
select(term_name, term_id, source, everything()) %>%
filter(term_size >= 5, term_size <= 350, intersection_size >= 3)gostplot(gostres, capped = FALSE, interactive = TRUE)OpenTargets genes sorted by globalScore
d9.mi <- otensg.mi %>%
drop_na(globalScore) %>%
filter(globalScore >= quantile(globalScore, 0.90)) %>%
sample_frac(1L) %>% # randomize row order before arranging
arrange(desc(globalScore))
d9.miquery <- d9.mi %>% distinct(ensembl_gene_id, .keep_all = TRUE) %>% pull(ensembl_gene_id)
writeLines(query, "alt/enrichmentmap/opentargets.mi.genes.global_score.query.txt")
multiquery <- c(multiquery, "> opentargets.mi.genes.global_score", query)
gostres <- gost(
query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
sources = c("GO:BP", "KEGG", "REAC", "WP", "HP"),
correction_method = "gSCS")
gostres$result %>%
select(term_name, term_id, source, everything()) %>%
filter(term_size >= 5, term_size <= 350, intersection_size >= 3)gostplot(gostres, capped = FALSE, interactive = TRUE)Write multiquery to file for later use in enrichmentmap
writeLines(multiquery, "alt/enrichmentmap/multiquery.txt")Print environment
sessioninfo::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.4.3 (2025-02-28)
os macOS Sequoia 15.3.2
system aarch64, darwin20
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/New_York
date 2025-03-20
pandoc 3.2 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/aarch64/ (via rmarkdown)
quarto 1.6.42 @ /usr/local/bin/quarto
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
abind 1.4-8 2024-09-12 [1] CRAN (R 4.4.1)
backports 1.5.0 2024-05-23 [1] CRAN (R 4.4.0)
Biobase 2.64.0 2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
BiocGenerics 0.50.0 2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
BiocIO 1.14.0 2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
BiocParallel 1.38.0 2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
Biostrings 2.72.1 2024-06-02 [1] Bioconductor 3.19 (R 4.4.0)
bit 4.6.0 2025-03-06 [1] CRAN (R 4.4.1)
bit64 4.6.0-1 2025-01-16 [1] CRAN (R 4.4.1)
bitops 1.0-9 2024-10-03 [1] CRAN (R 4.4.1)
broom * 1.0.7 2024-09-26 [1] CRAN (R 4.4.1)
cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.4.0)
cli 3.6.4 2025-02-13 [1] CRAN (R 4.4.1)
codetools 0.2-20 2024-03-31 [2] CRAN (R 4.4.3)
colorspace 2.1-1 2024-07-26 [1] CRAN (R 4.4.0)
crayon 1.5.3 2024-06-20 [1] CRAN (R 4.4.0)
crosstalk 1.2.1 2023-11-23 [1] CRAN (R 4.4.0)
curl 6.2.1 2025-02-19 [1] CRAN (R 4.4.1)
data.table 1.17.0 2025-02-22 [1] CRAN (R 4.4.1)
DelayedArray 0.30.1 2024-05-30 [1] Bioconductor 3.19 (R 4.4.0)
digest 0.6.37 2024-08-19 [1] CRAN (R 4.4.1)
dplyr * 1.1.4 2023-11-17 [1] CRAN (R 4.4.0)
evaluate 1.0.3 2025-01-10 [1] CRAN (R 4.4.1)
farver 2.1.2 2024-05-13 [1] CRAN (R 4.4.0)
fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.4.0)
forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.4.0)
generics 0.1.3 2022-07-05 [1] CRAN (R 4.4.0)
GenomeInfoDb 1.40.1 2024-05-24 [1] Bioconductor 3.19 (R 4.4.0)
GenomeInfoDbData 1.2.12 2024-06-15 [1] Bioconductor
GenomicAlignments 1.40.0 2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
GenomicRanges 1.56.2 2024-10-09 [1] Bioconductor 3.19 (R 4.4.1)
ggformula * 0.12.0 2023-11-09 [1] CRAN (R 4.4.0)
ggplot2 * 3.5.1 2024-04-23 [1] CRAN (R 4.4.0)
ggridges * 0.5.6 2024-01-23 [1] CRAN (R 4.4.0)
glue 1.8.0 2024-09-30 [1] CRAN (R 4.4.1)
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* ── Packages attached to the search path.
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